{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Forecasting with TSMixer\n",
    "> Tutorial on how to do multivariate forecasting using TSMixer models."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In _multivariate_ forecasting, we use the information from every time series to produce all forecasts for all time series jointly. In contrast, in _univariate_ forecasting we only consider the information from every individual time series and produce forecasts for every time series separately. Multivariate forecasting methods thus use more information to produce every forecast, and thus should be able to provide better forecasting results. However, multivariate forecasting methods also scale with the number of time series, which means these methods are commonly less well suited for large-scale problems (i.e. forecasting many, many time series).\n",
    "\n",
    "In this notebook, we will demonstrate the performance of a state-of-the-art multivariate forecasting architecture `TSMixer` / `TSMixerx` when compared to a univariate forecasting method (`NHITS`) and a simple MLP-based multivariate method (`MLPMultivariate`).\n",
    "\n",
    "We will show how to:\n",
    "* Load the [ETTm2](https://github.com/zhouhaoyi/ETDataset) benchmark dataset, used in the academic literature.\n",
    "* Train a `TSMixer`, `TSMixerx` and `MLPMultivariate` model\n",
    "* Forecast the test set\n",
    "* Optimize the hyperparameters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/LongHorizon_with_Transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast datasetsforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load ETTm2 Data\n",
    "\n",
    "The `LongHorizon` class will automatically download the complete ETTm2 dataset and process it.\n",
    "\n",
    "It return three Dataframes: `Y_df` contains the values for the target variables, `X_df` contains exogenous calendar features and `S_df` contains static features for each time-series (none for ETTm2). For this example we will use `Y_df` and `X_df`. \n",
    "\n",
    "In `TSMixerx`, we can make use of the additional exogenous features contained in `X_df`. In `TSMixer`, there is _no_ support for exogenous features. Hence, if you want to use exogenous features, you should use `TSMixerx`. \n",
    "\n",
    "If you want to use your own data just replace `Y_df` and `X_df`. Be sure to use a long format and make sure to have a similar structure as our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from datasetsforecast.long_horizon import LongHorizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change this to your own data to try the model\n",
    "Y_df, X_df, _ = LongHorizon.load(directory='./', group='ETTm2')\n",
    "Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "\n",
    "# X_df contains the exogenous features, which we add to Y_df\n",
    "X_df['ds'] = pd.to_datetime(X_df['ds'])\n",
    "Y_df = Y_df.merge(X_df, on=['unique_id', 'ds'], how='left')\n",
    "\n",
    "# We make validation and test splits\n",
    "n_time = len(Y_df.ds.unique())\n",
    "val_size = int(.2 * n_time)\n",
    "test_size = int(.2 * n_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>ex_1</th>\n",
       "      <th>ex_2</th>\n",
       "      <th>ex_3</th>\n",
       "      <th>ex_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>-0.041413</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.185467</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:30:00</td>\n",
       "      <td>-0.257495</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:45:00</td>\n",
       "      <td>-0.577510</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 01:00:00</td>\n",
       "      <td>-0.385501</td>\n",
       "      <td>-0.456522</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403195</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 22:45:00</td>\n",
       "      <td>-1.581325</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403196</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:00:00</td>\n",
       "      <td>-1.581325</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403197</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:15:00</td>\n",
       "      <td>-1.581325</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403198</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:30:00</td>\n",
       "      <td>-1.562328</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403199</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:45:00</td>\n",
       "      <td>-1.562328</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       unique_id                  ds         y      ex_1      ex_2      ex_3  \\\n",
       "0           HUFL 2016-07-01 00:00:00 -0.041413 -0.500000  0.166667 -0.500000   \n",
       "1           HUFL 2016-07-01 00:15:00 -0.185467 -0.500000  0.166667 -0.500000   \n",
       "2           HUFL 2016-07-01 00:30:00 -0.257495 -0.500000  0.166667 -0.500000   \n",
       "3           HUFL 2016-07-01 00:45:00 -0.577510 -0.500000  0.166667 -0.500000   \n",
       "4           HUFL 2016-07-01 01:00:00 -0.385501 -0.456522  0.166667 -0.500000   \n",
       "...          ...                 ...       ...       ...       ...       ...   \n",
       "403195        OT 2018-02-20 22:45:00 -1.581325  0.456522 -0.333333  0.133333   \n",
       "403196        OT 2018-02-20 23:00:00 -1.581325  0.500000 -0.333333  0.133333   \n",
       "403197        OT 2018-02-20 23:15:00 -1.581325  0.500000 -0.333333  0.133333   \n",
       "403198        OT 2018-02-20 23:30:00 -1.562328  0.500000 -0.333333  0.133333   \n",
       "403199        OT 2018-02-20 23:45:00 -1.562328  0.500000 -0.333333  0.133333   \n",
       "\n",
       "            ex_4  \n",
       "0      -0.001370  \n",
       "1      -0.001370  \n",
       "2      -0.001370  \n",
       "3      -0.001370  \n",
       "4      -0.001370  \n",
       "...          ...  \n",
       "403195 -0.363014  \n",
       "403196 -0.363014  \n",
       "403197 -0.363014  \n",
       "403198 -0.363014  \n",
       "403199 -0.363014  \n",
       "\n",
       "[403200 rows x 7 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Train models\n",
    "\n",
    "We will train models using the `cross_validation` method, which allows users to automatically simulate multiple historic forecasts (in the test set).\n",
    "\n",
    "The `cross_validation` method will use the validation set for hyperparameter selection and early stopping, and will then produce the forecasts for the test set.\n",
    "\n",
    "First, instantiate each model in the `models` list, specifying the `horizon`, `input_size`, and training iterations. In this notebook, we compare against the univariate `NHITS` and multivariate `MLPMultivariate` models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "import torch\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.models import TSMixer, TSMixerx, NHITS, MLPMultivariate\n",
    "from neuralforecast.losses.pytorch import MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)\n",
    "torch.set_float32_matmul_precision('high')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "horizon = 96\n",
    "input_size = 512\n",
    "models = [\n",
    "          TSMixer(h=horizon,\n",
    "                input_size=input_size,\n",
    "                n_series=7,\n",
    "                max_steps=1000,\n",
    "                val_check_steps=100,\n",
    "                early_stop_patience_steps=5,\n",
    "                scaler_type='identity',\n",
    "                valid_loss=MAE(),\n",
    "                random_seed=12345678,\n",
    "                ),  \n",
    "          TSMixerx(h=horizon,\n",
    "                input_size=input_size,\n",
    "                n_series=7,\n",
    "                max_steps=1000,\n",
    "                val_check_steps=100,\n",
    "                early_stop_patience_steps=5,\n",
    "                scaler_type='identity',\n",
    "                dropout=0.7,\n",
    "                valid_loss=MAE(),\n",
    "                random_seed=12345678,\n",
    "                futr_exog_list=['ex_1', 'ex_2', 'ex_3', 'ex_4'],\n",
    "                ),\n",
    "          MLPMultivariate(h=horizon,\n",
    "                input_size=input_size,\n",
    "                n_series=7,\n",
    "                max_steps=1000,\n",
    "                val_check_steps=100,\n",
    "                early_stop_patience_steps=5,\n",
    "                scaler_type='standard',\n",
    "                hidden_size=256,\n",
    "                valid_loss=MAE(),\n",
    "                random_seed=12345678,\n",
    "                ),                                             \n",
    "           NHITS(h=horizon,\n",
    "                input_size=horizon,\n",
    "                max_steps=1000,\n",
    "                val_check_steps=100,\n",
    "                early_stop_patience_steps=5,\n",
    "                scaler_type='robust',\n",
    "                valid_loss=MAE(),\n",
    "                random_seed=12345678,\n",
    "                ),                                                                       \n",
    "         ]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "Check our `auto` models for automatic hyperparameter optimization, and see the end of this tutorial for an example of hyperparameter tuning.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate a `NeuralForecast` object with the following required parameters:\n",
    "\n",
    "* `models`: a list of models.\n",
    "\n",
    "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n",
    "\n",
    "Second, use the `cross_validation` method, specifying the dataset (`Y_df`), validation size and test size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(\n",
    "    models=models,\n",
    "    freq='15min',\n",
    ")\n",
    "\n",
    "Y_hat_df = nf.cross_validation(\n",
    "    df=Y_df,\n",
    "    val_size=val_size,\n",
    "    test_size=test_size,\n",
    "    n_windows=None,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `cross_validation` method will return the forecasts for each model on the test set."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evaluate Results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot the forecasts on the test set for the `OT` variable for all models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utilsforecast.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoffs = Y_hat_df['cutoff'].unique()[::horizon]\n",
    "Y_plot = Y_hat_df[Y_hat_df['cutoff'].isin(cutoffs)].drop(columns='cutoff')\n",
    "plot_series(forecasts_df=Y_plot, ids=['OT'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we compute the test errors using the Mean Absolute Error (MAE) and Mean Squared Error (MSE):\n",
    "\n",
    "$\\qquad MAE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} |y_{\\tau} - \\hat{y}_{\\tau}| \\qquad$ and $\\qquad MSE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} (y_{\\tau} - \\hat{y}_{\\tau})^{2} \\qquad$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utilsforecast.evaluation import evaluate\n",
    "from utilsforecast.losses import mae, mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>TSMixer</th>\n",
       "      <th>TSMixerx</th>\n",
       "      <th>MLPMultivariate</th>\n",
       "      <th>NHITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>0.245435</td>\n",
       "      <td>0.249727</td>\n",
       "      <td>0.263579</td>\n",
       "      <td>0.251008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mse</td>\n",
       "      <td>0.162566</td>\n",
       "      <td>0.163098</td>\n",
       "      <td>0.176594</td>\n",
       "      <td>0.178864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  metric   TSMixer  TSMixerx  MLPMultivariate     NHITS\n",
       "0    mae  0.245435  0.249727         0.263579  0.251008\n",
       "1    mse  0.162566  0.163098         0.176594  0.178864"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(Y_hat_df.drop(columns='cutoff'), metrics=[mae, mse], agg_fn='mean')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference, we can check the performance when compared to self-reported performance in the paper. We find that `TSMixer` provides better results than the _univariate_ method `NHITS`. Also, our implementation of `TSMixer` very closely tracks the results of the original paper. Finally, it seems that there is little benefit of using the additional exogenous variables contained in the dataframe `X_df` as `TSMixerx` performs worse than `TSMixer`, especially on longer horizons. Note also that `MLPMultivariate` clearly underperforms as compared to the other methods, which can be somewhat expected given its relative simplicity.\n",
    "\n",
    "Mean Absolute Error (MAE)\n",
    "\n",
    "| Horizon   |TSMixer<br> (this notebook) | TSMixer <br>(paper) | TSMixerx<br> (this notebook) |  NHITS <br>(this notebook)    | NHITS <br>(paper)  | MLPMultivariate <br>(this notebook) \n",
    "|---        |---        |---        |---        |---        |---        |---\n",
    "|  96       | **0.245** | 0.252     | 0.250     |  0.251    |   0.251   | 0.263      \n",
    "|  192      | **0.288** | 0.290     | 0.300     |  0.291    |   0.305   | 0.361\n",
    "|  336      | **0.323** | 0.324     | 0.380     |  0.344    |   0.346   | 0.390 \n",
    "|  720      | **0.377** | 0.422     | 0.464     |  0.417    |   0.413   | 0.608\n",
    "\n",
    "Mean Squared Error (MSE)\n",
    "\n",
    "| Horizon   |TSMixer<br> (this notebook) | TSMixer <br>(paper) | TSMixerx<br> (this notebook) | NHITS <br>(this notebook)    | NHITS <br>(paper) | MLPMultivariate <br>(this notebook) \n",
    "|---        |---        |---            |---        |---                |---        |---            \n",
    "|  96       | **0.163** | **0.163**     | 0.163     |  0.179            |   0.179   | 0.177\n",
    "|  192      | 0.220     | **0.216**     | 0.231     |  0.239            |   0.245   | 0.330  \n",
    "|  336      | 0.272     | **0.268**     | 0.361     |  0.311            |   0.295   | 0.376  \n",
    "|  720      | **0.356** | 0.420         | 0.493     |  0.451            |   0.401   | 3.421 \n",
    "\n",
    "Note that for the table above, we use the same hyperparameters for all methods for all horizons, whereas the original papers tune the hyperparameters for each horizon."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Tuning the hyperparameters\n",
    "The `AutoTSMixer` / `AutoTSMixerx` class will automatically perform hyperparamter tunning using the [Tune library](https://docs.ray.io/en/latest/tune/index.html), exploring a user-defined or default search space. Models are selected based on the error on a validation set and the best model is then stored and used during inference. \n",
    "\n",
    "The `AutoTSMixer.default_config` / `AutoTSMixerx.default_config`  attribute contains a suggested hyperparameter space. Here, we specify a different search space following the paper's hyperparameters. Feel free to play around with this space.\n",
    "\n",
    "For this example, we will optimize the hyperparameters for `horizon = 96`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray import tune\n",
    "from ray.tune.search.hyperopt import HyperOptSearch\n",
    "from neuralforecast.auto import AutoTSMixer, AutoTSMixerx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "horizon = 96 # 24hrs = 4 * 15 min.\n",
    "\n",
    "tsmixer_config = {\n",
    "       \"input_size\": input_size,                                                 # Size of input window\n",
    "       \"max_steps\": tune.choice([500, 1000, 2000]),                              # Number of training iterations\n",
    "       \"val_check_steps\": 100,                                                   # Compute validation every x steps\n",
    "       \"early_stop_patience_steps\": 5,                                           # Early stopping steps\n",
    "       \"learning_rate\": tune.loguniform(1e-4, 1e-2),                             # Initial Learning rate\n",
    "       \"n_block\": tune.choice([1, 2, 4, 6, 8]),                                  # Number of mixing layers\n",
    "       \"dropout\": tune.uniform(0.0, 0.99),                                       # Dropout\n",
    "       \"ff_dim\": tune.choice([32, 64, 128]),                                     # Dimension of the feature linear layer\n",
    "       \"scaler_type\": 'identity',       \n",
    "    }\n",
    "\n",
    "tsmixerx_config = tsmixer_config.copy()\n",
    "tsmixerx_config['futr_exog_list'] = ['ex_1', 'ex_2', 'ex_3', 'ex_4']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To instantiate `AutoTSMixer` and `AutoTSMixerx` you need to define:\n",
    "\n",
    "* `h`: forecasting horizon\n",
    "* `n_series`: number of time series in the multivariate time series problem.\n",
    "\n",
    "In addition, we define the following parameters (if these are not given, the `AutoTSMixer`/`AutoTSMixerx` class will use a pre-defined value):\n",
    "* `loss`: training loss. Use the `DistributionLoss` to produce probabilistic forecasts.\n",
    "* `config`: hyperparameter search space. If `None`, the `AutoTSMixer` class will use a pre-defined suggested hyperparameter space.\n",
    "* `num_samples`: number of configurations explored. For this example, we only use a limited amount of `10`.\n",
    "* `search_alg`: type of search algorithm used for selecting parameter values within the hyperparameter space.\n",
    "* `backend`: the backend used for the hyperparameter optimization search, either `ray` or `optuna`. \n",
    "* `valid_loss`: the loss used for the validation sets in the optimization procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoTSMixer(h=horizon,\n",
    "                    n_series=7,\n",
    "                    loss=MAE(),\n",
    "                    config=tsmixer_config,\n",
    "                    num_samples=10,\n",
    "                    search_alg=HyperOptSearch(),\n",
    "                    backend='ray',\n",
    "                    valid_loss=MAE())\n",
    "\n",
    "modelx = AutoTSMixerx(h=horizon,\n",
    "                    n_series=7,\n",
    "                    loss=MAE(),\n",
    "                    config=tsmixerx_config,\n",
    "                    num_samples=10,\n",
    "                    search_alg=HyperOptSearch(),\n",
    "                    backend='ray',\n",
    "                    valid_loss=MAE())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we fit the model by instantiating a `NeuralForecast` object with the following required parameters:\n",
    "\n",
    "* `models`: a list of models.\n",
    "\n",
    "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n",
    "\n",
    "The `cross_validation` method allows you to simulate multiple historic forecasts, greatly simplifying pipelines by replacing for loops with `fit` and `predict` methods.\n",
    "\n",
    "With time series data, cross validation is done by defining a sliding window across the historical data and predicting the period following it. This form of cross validation allows us to arrive at a better estimation of our model’s predictive abilities across a wider range of temporal instances while also keeping the data in the training set contiguous as is required by our models.\n",
    "\n",
    "The `cross_validation` method will use the validation set for hyperparameter selection, and will then produce the forecasts for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(models=[model, modelx], freq='15min')\n",
    "Y_hat_df = nf.cross_validation(df=Y_df, val_size=val_size,\n",
    "                               test_size=test_size, n_windows=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Evaluate Results\n",
    "\n",
    "The `AutoTSMixer`/`AutoTSMixerx` class contains a `results` attribute that stores information of each configuration explored. It contains the validation loss and best validation hyperparameter. The result dataframe `Y_hat_df` that we obtained in the previous step is based on the best config of the hyperparameter search. For `AutoTSMixer`, the best config is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_size': 512,\n",
       " 'max_steps': 2000,\n",
       " 'val_check_steps': 100,\n",
       " 'early_stop_patience_steps': 5,\n",
       " 'learning_rate': 0.00034884229033995355,\n",
       " 'n_block': 4,\n",
       " 'dropout': 0.7592667651473878,\n",
       " 'ff_dim': 128,\n",
       " 'scaler_type': 'identity',\n",
       " 'n_series': 7,\n",
       " 'h': 96,\n",
       " 'loss': MAE(),\n",
       " 'valid_loss': MAE()}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf.models[0].results.get_best_result().config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and for `AutoTSMixerx`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_size': 512,\n",
       " 'max_steps': 2000,\n",
       " 'val_check_steps': 100,\n",
       " 'early_stop_patience_steps': 5,\n",
       " 'learning_rate': 0.00019039338576148522,\n",
       " 'n_block': 6,\n",
       " 'dropout': 0.5902743834953548,\n",
       " 'ff_dim': 128,\n",
       " 'scaler_type': 'identity',\n",
       " 'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'),\n",
       " 'n_series': 7,\n",
       " 'h': 96,\n",
       " 'loss': MAE(),\n",
       " 'valid_loss': MAE()}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf.models[1].results.get_best_result().config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute the test errors of the best config for the two metrics of interest:\n",
    "\n",
    "$\\qquad MAE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} |y_{\\tau} - \\hat{y}_{\\tau}| \\qquad$ and $\\qquad MSE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} (y_{\\tau} - \\hat{y}_{\\tau})^{2} \\qquad$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>AutoTSMixer</th>\n",
       "      <th>AutoTSMixerx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>0.243749</td>\n",
       "      <td>0.251972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mse</td>\n",
       "      <td>0.162212</td>\n",
       "      <td>0.164347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  metric  AutoTSMixer  AutoTSMixerx\n",
       "0    mae     0.243749      0.251972\n",
       "1    mse     0.162212      0.164347"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(Y_hat_df.drop(columns='cutoff'), metrics=[mae, mse], agg_fn='mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare the error metrics for our optimized setting to the earlier setting in which we used the default hyperparameters. In this case, for a horizon of 96, we got slightly improved results for `TSMixer` on `MAE`. Interestingly, we did not improve for `TSMixerx` as compared to the default settings. For this dataset, it seems there is limited value in using exogenous features with the `TSMixerx` architecture for a horizon of 96.\n",
    "\n",
    "| Metric    |TSMixer<br> (optimized) | TSMixer <br>(default)  | TSMixer <br>(paper)   |TSMixerx<br> (optimized) | TSMixerx <br>(default) \n",
    "|---        |---                     |---                     |---                    |---                      |---\n",
    "| MAE       | **0.244**              | 0.245                  | 0.252                 | 0.252                   | 0.250 \n",
    "| MSE       | **0.162**              | 0.163                  | 0.163                 | 0.164                   | 0.163\n",
    "\n",
    "Note that we only evaluated 10 hyperparameter configurations (`num_samples=10`), which may suggest that it is possible to further improve forecasting performance by exploring more hyperparameter configurations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[Chen, Si-An, Chun-Liang Li, Nate Yoder, Sercan O. Arik, and Tomas Pfister (2023). \"TSMixer: An All-MLP Architecture for Time Series Forecasting.\"](http://arxiv.org/abs/2303.06053) <br>\n",
    "[Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  }
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